STL vs MIL prediction for May 25, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects MIL 4.3 - STL 4.5. MIL is favored with a 50.6% win probability. The run line is -1.5 and the total is 7.5. Model projects 8.9 total runs.
MIL
4.3
Projected Score
VS
O/U 7.5
STL
4.5
Projected Score
Win Probability
MILSTL
-1.5
Run Line (MIL)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.5% (2,610 games)
Projected Runs Range 10th – 90th percentile
STL
346
MIL
246
Projected
MIL 4.3 — STL 4.5
Actual
MIL 5 — STL 1
Starting Pitcher Matchup
Matthew Liberatore L
STL
FF32%94 mph9% whiff
SL25%86 mph34% whiff
CH16%89 mph22% whiff
Jacob Misiorowski R
MIL
FF61%100 mph40% whiff
SL24%94 mph24% whiff
CU13%87 mph39% whiff
Weather Impact
American Family Field
81°F6 mph windRoof: retractable
HR: 1.016 Total: 1.006
thin air
Bullpen Comparison
STL
4.38ERA
4.36FIP
8.15K/9
4.53BB/9
1.39WHIP
MIL
3.67ERA
3.24FIP
9.66K/9
4.27BB/9
1.34WHIP
Betting Edges
ML AWAY
+31.5% EV
+184
RUN_LINE AWAY +1.5
-27.0% EV
-120
RUN_LINE HOME -1.5
-25.3% EV
+100
ML HOME
-22.2% EV
-222
TOTAL UNDER 7.5
-14.7% EV
-110
F5_ML HOME
-14.2% EV
-233
First 5 Innings & NRFI
STL F5
2.1 runs
35.5% win
MIL F5
2.5 runs
48.3% win
F5 Total
4.6
NRFI
57.4%
YRFI
42.6%
Avg 1st Inn Runs
0.87
HR Spotlight
Avg HRs
1.9
Over 0.5 HR
85%
Over 1.5 HR
58%
No HR
15%
Andrew Vaughn MIL24.6%
ISO: 0.188 | Barrel: 7.4% | vs Matthew Liberatore | Platoon: 1.12x
Brice Turang MIL20.7%
ISO: 0.056 | Barrel: 9.8% | vs Matthew Liberatore
Jordan Walker STL18.0%
ISO: 0.278 | Barrel: 16.8% | vs Jacob Misiorowski
Pitcher Strikeout Projections
Matthew Liberatore
0.0 K projected
STL | K/9: 0.0
Jacob Misiorowski
0.0 K projected
MIL | K/9: 0.0
Injury Report
STL8 injured
Nathan Church LF10-DAY-IL
Ramon Urias 3B10-DAY-IL
Lars Nootbaar LF60-DAY-IL
Packy Naughton RPDAY-TO-DAY
Sem Robberse SPDAY-TO-DAY
Victor Santos RPDAY-TO-DAY
+2 more
MIL8 injured
Logan Henderson SPDAY-TO-DAY
Quinn Priester SP15-DAY-IL
Brandon Woodruff SP15-DAY-IL
Rob Zastryzny RP60-DAY-IL
Jared Koenig RP15-DAY-IL
Brandon Lockridge LF10-DAY-IL
+2 more
AI Intelligence Analysis
NEUTRAL -2
DATA_INTEGRITY FAILURE: Model leans STL AWAY +31.5% edge (46.3% model prob) despite Misiorowski (A- pitcher, 0.826 grade, 13.9 K/9, 37.3% K-rate) vs Liberatore (B- pitcher, 0.448 grade, 7.6 K/9). This is bottom-tier starting pitcher vs ace — market correctly prices MIL home favorite at -222 (69% implied). Ace-at-home beats back-end arm; market has it right.
Key Factors
- Pitcher reality check: Misiorowski (A- grade, 0.826 overall_score, 13.9 K/9) is ELITE. Liberatore (B- grade, 0.448 overall_score, 7.6 K/9) is pedestrian. This is NOT a close matchup; Misiorowski outclasses.
- Market correctly priced MIL -222 (69% implied home win). Model saying STL 46.3% implies market undervaluing STL by 11.1 points — IMPLAUSIBLE when model SP analysis shows clear home ace advantage.
- historically weak + away ML: Model recommending away underdog historically weak — historical money pit.
- Post-game validation: Brewers won 5-1 handily. Misiorowski matched career high 12 K, carried no-hitter into 6th. This was not an edge opportunity; home ace crushed weak visitor arm as expected.
Risk Factors
- Model disagreement with pitcher-driven market is core issue. When model projects 46.3% win prob AGAINST a -222 favorite, ask: 'Does my simulation know something the market doesn't?' Answer: NO. Misiorowski is clearly elite (A- grade, 13.9 K/9), Liberatore clearly weak (B- grade). Market is right.
- Recommending STL away underdog at +184 would be 'fading the ace' play — these routinely underperform.
- AWAY historically weak + HIGH EDGE = classic data integrity failure pattern.
PITCHER MISMATCHDATA INTEGRITYMODEL MARKET CONFLICTSTRONG AVOID
Edge Analysis
Moneyline
MIL 50.6%
-25.3 pts
Run Line
-1.5
-25.3 pts
Total
7.5
+5.7 pts
How this prediction was generated: This page shows output from the Olympus Bets MLB Baseball Monte Carlo engine. Each game is simulated 10,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →